Almost every day, more and more aspects of human life become automated or computer-assisted. For example, applications that recommend parking places, restaurants, and driving directions are now widely available. Such applications have become possible because of the wider availability of diverse, interchangeable data sources, such as real-time traffic data and rating data from diners at restaurants.
With vast amounts of such data available at their disposal, decision systems have become more prevalent that help fully or partially automate the selection of many routine options that individual once made themselves. For example, rather than an individual having to decide the best of many possible ways to get from point A to point B, driving direction applications can now calculate several best routes from travel times and distances determined from real-time traffic data.
However, user input as to which of several competing criteria to optimize is often still needed by these decision systems. In a driving direction application, for example, factors such as a user's preference for low toll costs, for short distances over time, a preference for highways over side streets, etc., can make a vast difference in a selected driving route even when perfect information about traffic conditions is available. Furthermore, a user's preference as to which criteria to optimize often varies on a case-by-case basis; therefore, the need for user input may not be minimized even in a decision system that includes machine-learning techniques.
Therefore, easy-to-use and intuitive techniques and systems for allowing a diversity of decision systems to gather a user's input regarding their objectives are needed.